The invention disclosed and claimed herein is generally directed to a method for correcting spatial inhomogeneity or nonuniformity of spatial intensity in an acquired magnetic resonance (MR) or other medical diagnostic image. More particularly, the invention is directed to a correction method of such type wherein the primary component of inhomogeneity is slowly varying.
In many areas of imaging including MR and computed tomography (CT), acquired images are corrupted by slowly varying multiplicative inhomogeneities or nonuniformities in spatial intensity. Such nonuniformities can hinder visualization of the entire image at a given time, and can also hinder automated image analysis. Such inhomogeneity is a particular concern in MR when single or multiple surface coils are used to acquire imaging data. The acquired images generally contain intensity variations resulting from the inhomogeneous sensitivity profiles of the surface coil or coils. In general, tissue next to the surface coil appears much brighter than tissue far from the coil. Spatial intensity variations introduced by surface coil nonuniformity hinders visualization because one cannot find a window/level adjustment to encompass the entire field of view. When such images are filmed, the operator tries to select a setting which covers most of the features of interest. Furthermore, uncorrected image inhomogeneity makes it difficult to perform image segmentation and other aspects of image analysis.
An example of the problem is spine imaging, wherein one or more surface coils are placed behind a patient. If the central spinal canal is filmed optimally, tissue structure behind the vertebral column may be overamplified and may become so bright that no tissue detail can be seen. At the same time, tissue in front of the vertebral column may be so dark that image detail in that area is also obscured. Therefore, in order to optimally display and film the entire image, the signal variation due to the inhomogeneous sensitivity profile of the surface coil needs to be corrected. Surface coil image signal intensities generally represent the product of (1) precessing magnetization of the body tissue or other object being imaged, and (2) the sensitivity profile of the surface coil. Accordingly, various intensity correction algorithms have been devised to correct surface coil images by dividing out an estimate of the surface coil's sensitivity profile. Thus, if the observed or acquired MR image signal is defined in a spatial domain for a pixel location (x,y) by a function g(x,y) then g(x,y)=h(x,y)*f(x,y)+n(x,y), where * represents multiplication, h function., f function and n represent the coil profile function, a corrected function, and the imaging noise, respectively. More specifically, the corrected function is a function defining an image which is substantially free of distortion resulting from the inhomogeneity. Thus, the problem is to determine both h and f, given only the measured or acquired function g in the presence of n. However, if the function h can be determined which reasonably represents the inhomogeneity distortion, then the function f can be readily computed from f=[g*h]/[h*h+ψ1], which is known in the art as Weiner filter solution, where ψ1 is a regularization parameter corresponding to the reciprocal of signal to noise ratio.
The distortion arising from use of surface coils generally varies slowly over space. An important class of prior art solutions to the above problem is based on this assumption. In accordance therewith, a low pass filtering operation is applied to g. The resulting function, represented as LPF[g], does not contain high frequency components and is taken as an estimate of distortion function h. An estimate of the function f is then obtained by dividing g by LPF[g], i.e., f=g/LPF[g]. However, for this class of methods to be effective, g must not contain sharp intensity transitions. Unfortunately, in MR imaging an air-lipid interface usually contains sharp intensity transitions which violate the basic assumption made in the method, i.e., that the low frequency content in the scene being imaged is solely due to h. Significant air-lipid interferences will generally be encountered, for example, at the edges of an organ, i.e., at the boundary between the organ and an air-space or cavity.
To overcome the above deficiency in low pass filtering correction at the edge or boundary of an organ or other tissue structure, certain hybrid filtering techniques have been developed. Some of such techniques are set forth in the following references: Surface Coil MR Imaging of the Human Brain with an Analytic Reception Profile Correction, JMRI 5, 139-144, by S. E. Moyher, D. B. Vigeron, and S. J. Nelson; Phased Array Detectors and an Automated Intensity Correction Algorithm for High Resolution MR Imaging of the Human Brain, JMRI (1995), by L L. Wald, L. Carvajal, S. E. Moyher, S. J. Nelson, P. E. Grant, A. J. Barkovich, and D. B. Vingeron; Phased Array Image Intensity Correction: An Algorithm to Remove Intensity Variations in MR Images Resulting from the Inhomogeneous Sensitivity Profiles of Phased Array Surface Coils, a Master's thesis by J. Murakami (1995), University of Washington, Seattle, and in U.S. Pat. No. 5,943,433.
Intensity inhomogeneities can influence visualization of digital images. If there are multiple kinds of inhomogeneities in a digital image, several difficulties arise while compensating for such inhomogeneities. As an illustration, if there are two different kinds of inhomogeneities in a digital image, a designed algorithm may not be able to properly multiple inhomogeneities since the algorithm is tuned to mitigate one kind of inhomogeneity. Inadequate compensation can result in unsatisfactory visual appearance of the compensated image. If any specific scale is used for intensity correction, it is inadequate due to the reasons stated above. On the other hand, if an entire image is used for the intensity correction, it results in a flat image without any contrast. Moreover, if a normalization based on average intensity is performed on digital images, inconsistent results can occur between different slices of the digital images.